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Record W4245036440 · doi:10.22215/etd/2020-14231

The Role of Regulations in Decisions Regarding the Transition to Organic, Biodynamic, and Sustainable Agricultural Productions: The Case of Niagara Vineyards

2020· dissertation· en· W4245036440 on OpenAlexaff
Tess MacMillan

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldBusiness, Management and Accounting
TopicWine Industry and Tourism
Canadian institutionsCarleton University
Fundersnot available
KeywordsSustainabilityCertificationAgricultureBusinessProcess (computing)Sustainable agricultureOrganic certificationMarketingOrganic farmingTransition (genetics)Environmental planningEnvironmental resource managementGeographyManagementEconomicsEcology

Abstract

fetched live from OpenAlex

There is a myriad of motivations and barriers that influence agricultural producers to adopt ecologically-sound management practices.Therefore, this research aims to explore vintners' motivations to adopt organic, biodynamic, and sustainable practices in the Niagara region.This research also investigates what the challenges are in the transition process, and whether the regulations facilitate or hinder producers from completing the transition.This research employs a survey of regulations, semi-structured interviews, and online questionnaires to acquire data on vintners' experiences.The results conclude that Niagara's wineries are motivated to adopt ecologically-sound management practices due to environmental, social, and economic reasons.The results also indicate that there is a paradox of sustainability among organic, biodynamic, and sustainable producers on which practice is considered environmentally friendly.Lastly, the findings reveal that the wineries did not encounter severe challenges during transition, however, they note that the regulatory requirements and certification process need to improve.iii Dedication This thesis is dedicated to my mother, Dr. Cara MacMillan for her constant love, faith, support, and guidance.You are the reason why I am where I am today.Thank you for instilling into me your drive, work ethic, and love of learning.their knowledge and guidance as I tackle this research.You two have taught me how to be a better

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.912
Threshold uncertainty score0.176

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0090.007
Scholarly communication0.0060.002
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.217
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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